Don’t Use Executable Decision Models – Part 4

Posted By: James Taylor | Posted On: 28th June 2018 |

This is the final part in my series on why we don’t recommend using executable decision models. Part 1 discussed the problem of additional model technical complexity required for executable models, resulting in business users disengagement. Part 2 detailed the challenges of reuse and maintenance with an executable model, and Part 3 outlined how executable decision models limit the use of AI and advanced analytics because they only consider decision logic, not other available technologies.

Our DecisionsFirst approach keeps the decision model focused on SMEs, prioritizes reuse and maintenance, and blends the right mix of decision-making technologies – decision logic, advanced analytics and AI, human decision making – to solve the business problem, using a Virtual Decision Hub.

Virtual Decision Hub

Our clients don’t have a single, monolithic implementation platform. Instead they have a couple of BRMSs, several analytic execution platforms, an ever-expanding AI platform, and many package modules. These all contain decision-making and our DecisionsFirst approach uses decision modeling to coordinate them rather than duplicate their behavior in an executable model. A virtual decision hub that uses a decision model to coordinate this kind of multi-platform environment is central to success in Decision Management.

It’s impractical for large organizations to replace all their decision logic with executable models – they have too much invested in packages and existing systems to rip all the logic out. Even if that was possible, the scale and complexity of a large organization makes it unlikely that executable models could be used, reused and maintained everywhere. Executable models are also not going to keep up with developments in analytics, ML and AI. Using decision modeling to coordinate, reuse and extend the decision-making embedded in packages and platforms offers a better ROI, creates opportunities to integrate the most advanced analytic and AI technology, and is a more cohesive long-term strategy for Decision Management.

Executable decision models are here and they have some great use cases – a standards organization can publish an executable decision model for instance. In addition, some of the platforms and packages being coordinated in a virtual decision hub may use executable models to define local decision logic- But executable decision models are not the be-all and end-all they are made out to be. A virtual decision hub based on mixing and matching existing and new decision technology with decision models is going to be a better fit, especially if you are a large, more complex organization.